Assessment of computational methods for predicting the effects of missense mutations in human cancers.

ABSTRACT:

Background

Recent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most observed missense changes are neutral passenger mutations. Various computational methods have been developed to predict the effects of amino acid substitutions on protein function and classify mutations as deleterious or benign. These include approaches that rely on evolutionary conservation, structural constraints, or physicochemical attributes of amino acid substitutions. Here we review existing methods and further examine eight tools: SIFT, PolyPhen2, Condel, CHASM, mCluster, logRE, SNAP, and MutationAssessor, with respect to their coverage, accuracy, availabili

SUBMITTER: Gnad F 

PROVIDER: S-EPMC3665581 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature